Monitoring and prediction of land use/land cover changes using CA-Markov model: a case study of Ravansar County in Iran

Human‐induced land use/land cover (LULC) changes are among the most important processes that shape the dynamics of the earth’s surface. This phenomenon, which is occurring at an astonishing rate, and its consequential environmental impacts have become an important area of research for scientists.Therefore, a wide range of methods and models have been developed to detect and predict these alterations, among which cellular automata (CA) models such as the CA‐Markov model, due to their affinity to geographic information system (GIS) and remote sensing (RS), are appropriate for detailed resolution modelling and simulating dynamic spatial processes. In Iran, the district of Ravansar has undergone severe LULC changes recently, thus to take the necessary precautions, decision‐makers need to predict and determine the extent of these changes. In this study, using spatial analysis methods the LULC changes in Ravansar were investigated from 1992 to 2015. Subsequently, the CA‐Markov model was applied to simulate the spatial pattern changes of LULC until 2030. Our results indicated that from 1992 to 2015, this region has witnessed a noticeable increase in the areas of the built‐up and agricultural lands (both aquatic and non‐aquatic), resulting in the decrease of the gardens, range, and bare lands. The simulated LULC map showed that this trend will continue due to more urbanization and development of agricultural areas.     

Application of Geochemistry and VNIR Spectroscopy in Mapping Heavy Metal Pollution of Stream Sediments in the Takab Mining Area, NW of Iran

This study considered the possibility of using visible and near infrared (VNIR) spectral absorption feature parameters (SAFPs) in predicting the concentration and mapping the distribution of heavy metals in sediments of the Takab area. In total, 60 sediment samples were collected along main streams draining from the mining districts and tailing sites, in order to measure the concentration of As, Co, V, Cu, Cr, Ni, Hg, Ti, Pb and Zn and the reflectance spectra (350–2500 nm). The quantitative relationship between SAFPs (Depth500nm, R610/500nm, R1344/778nm, Area500nm, Depth2200nm, Area2200nm, Asym2200nm) and geochemical data were assessed using stepwise multiple linear regression (SMLR) and enter multiple linear regression (EMLR) methods. The results showed a strong negative correlation between Ni and Cr with Area2200nm, a significant positive correlation between As and Asym2200nm, Ni and Co with Depth2200nm, as well as Co, V and total values with Depth500nm. The EMLR method eventuated in a significant prediction result for Ni, Cr, Co and As concentrations based on spectral parameters, whereas the prediction for Zn, V and total value was relatively weak. The spatial distribution pattern of geochemical data showed that mining activities, along with the natural weathering of base metal occurrences and rock units, has caused high concentrations of heavy metals in sediments of the Sarough River tributaries.     

Numerical ANFIS-Based Formulation for Prediction of the Ultimate Axial Load Bearing Capacity of Piles Through CPT Data

This study explores the potential of adaptive neuro-fuzzy inference systems (ANFIS) for prediction of the ultimate axial load bearing capacity of piles (P_u) using cone penetration test (CPT) data. In this regard, a reliable previously published database composed of 108 datasets was selected to develop ANFIS models. The collected database contains information regarding pile geometry, material, installation, full-scale static pile load test and CPT results for each sample. Reviewing the literature, several common and uncommon variables have been considered for direct or indirect estimation of P_u based on static pile load test, cone penetration test data or other in situ or laboratory testing methods. In present study, the pile shaft and tip area, the average cone tip resistance along the embedded length of the pile, the average cone tip resistance over influence zone and the average sleeve friction along the embedded length of the pile which are obtained from CPT data are considered as independent input variables where the output variable is P_u for the ANFIS model development. Besides, a notable criticism about ANFIS as a prediction tool is that it does not provide practical prediction equations. To tackle this issue, the obtained optimal ANFIS model is represented as a tractable equation which can be used via spread sheet software or hand calculations to provide precise predictions of P_u with the calculated correlation coefficient of 0.96 between predicted and experimental values for all of the data in this study. Considering several criteria, it is represented that the proposed model is able to estimate the output with a high degree of accuracy as compared to those results obtained by some direct CPT-based methods in the literature. Furthermore, in order to assess the capability of the proposed model from geotechnical engineering viewpoints, sensitivity and parametric analyses are done.     

Impact of the North Atlantic Oscillation on streamflow in Western Iran

The Middle East region, where arid and semi‐arid regions occupy most of the land, is extremely vulnerable to any natural or anthropogenic reductions in available water resources. Much of the observed interannual‐decadal variability in Middle Eastern streamflow is physically linked to a large‐scale atmospheric circulation patterns such as the North Atlantic Oscillation (NAO). In this work, the relationship between the NAO index and the seasonal and annual streamflows in the west of Iran was statistically examined during the last four decades. The correlations were constructed for two scenarios (with and without time lag). The associations between the annual and seasonal streamflows and the simultaneous NAO index were found to be poor and insignificant. The possibility of streamflow forecasting was also explored, and the results of lag correlations revealed that streamflow responses at the NAO signal with two and three seasons delays. The highest Spearman correlation coefficient of 0.379 was found between the spring NAO index and the autumn streamflow series at Taghsimab station, indicating that roughly 14% of the variance in the streamflow series is associated with NAO forcing. Copyright © 2013 John Wiley & Sons, Ltd.     

Climate change outlook for water resources management in a semiarid river basin: the effect of the environmental water demand

Iran is a developing country with arid and semiarid regions. Poor management of water resources combined with the effects of climate change is leading to the drying of several rivers and wetlands. Several planned water development projects, primarily for agricultural expansion, will be implemented in the coming years which could worsen impacts on vulnerable aquatic ecosystems. Proper water resources management is essential to meet present and future residential, environmental, industrial, and agricultural demands in semiarid regions. This paper presents projections of how the availability of water resources will change in the Karkheh river basin of Iran for the period 2010–2059 employing sustainability criteria in the form of time-based reliability, volumetric reliability, resiliency, and vulnerability. This paper’s results show that consideration of environmental receptors as a stakeholder of water use places limitations on agricultural development within the Karkheh river basin.     

Neuro-fuzzy reaping of shear wave velocity correlations derived by hybrid genetic algorithm-pattern search technique

Shear wave velocity is a critical physical property of rock, which provides significant data for geomechanical and geophysical studies. This study proposes a multi-step strategy to construct a model estimating shear wave velocity from conventional well log data. During the first stage, three correlation structures, including power law, exponential, and trigonometric were designed to formulate conventional well log data into shear wave velocity. Then, a Genetic Algorithm-Pattern Search tool was used to find the optimal coefficients of these correlations. Due to the different natures of these correlations, they might overestimate/underestimate in some regions relative to each other. Therefore, a neuro-fuzzy algorithm is employed to combine results of intelligently derived formulas. Neuro-fuzzy technique can compensate the effect of overestimation/underestimation to some extent, through the use of fuzzy rules. One set of data points was used for constructing the model and another set of unseen data points was employed to assess the reliability of the propounded model. Results have shown that the hybrid genetic algorithm-pattern search technique is a robust tool for finding the most appropriate form of correlations, which are meant to estimate shear wave velocity. Furthermore, neuro-fuzzy combination of derived correlations was capable of improving the accuracy of the final prediction significantly.     

Modeling Vapor Migration for Estimating the Time to Reach Steady State Conditions

The design and planning of soil vapor sampling for vapor intrusion assessment require an estimate of the time for vapor migration from the contamination source to reach steady state prior to vapor sampling and analysis for volatile organic compounds (VOCs). This study presents the model derivation, analytical solutions, as well as the assumptions and limitations of a one-dimensional VOC vapor transport model based on diffusion in porous media and equilibrium partitioning of VOCs in solid, aqueous, and vapor phases. The model assumes a finite domain with boundary conditions that represent the scenarios of vapor migration in the real environment. The derivation of the conceptual model is presented along with its practical use and implications as illustrated through case examples. Consideration of the upper (or exit) boundary condition along with the distance between the source and the applicable boundary, rather than the distance from the source to the measurement point, are shown to be critical in the time estimates as compared to an expression typically used and cited in guidance documents. The study reveals the importance of defining a conceptual model and relevant boundaries in assessing near steady state conditions, and suggests a tiered approach in refining the estimate with increasing level of effort for practical applications in vapor assessment.     

Relationship between DOC photochemistry and mercury redox transformations in temperate lakes and wetlands

We compared diurnal variations in dissolved gaseous mercury (DGM) concentration and in losses of dissolved organic carbon fluorescence (DOCF) in four boreal Canadian Shield lakes, in one beaver pond, and in filtered and unfiltered water from a wetland in Lake St. Pierre, a fluvial lake of the St. Lawrence River. These systems were chosen to represent a spectrum of DOC. We also determined the contribution of UVB, UVA and visible light on DGM photo-induced production in the four lakes. Our results showed a strong relationship between DGM concentrations and light intensity and between DGM production and losses in DOCF, in all study sites. We also observed higher rates of DGM formation and of DOCF bleaching in the presence of UV radiation. Under UVB light, production of DGM was higher in clear lakes than in the humic ones. Inversely, in the UVA range, DGM production tended to be higher in humic lakes. We suggest that DOCF bleaching can be used as a proxy for the rate of formation of reactive species that may alter the redox state of mercury in surface waters. We also have indications that DGM production is more important in clear than in humic waters.     

Prediction of Compressional, Shear, and Stoneley Wave Velocities from Conventional Well Log Data Using a Committee Machine with Intelligent Systems

Measurement of compressional, shear, and Stoneley wave velocities, carried out by dipole sonic imager (DSI) logs, provides invaluable data in geophysical interpretation, geomechanical studies and hydrocarbon reservoir characterization. The presented study proposes an improved methodology for making a quantitative formulation between conventional well logs and sonic wave velocities. First, sonic wave velocities were predicted from conventional well logs using artificial neural network, fuzzy logic, and neuro-fuzzy algorithms. Subsequently, a committee machine with intelligent systems was constructed by virtue of hybrid genetic algorithm-pattern search technique while outputs of artificial neural network, fuzzy logic and neuro-fuzzy models were used as inputs of the committee machine. It is capable of improving the accuracy of final prediction through integrating the outputs of aforementioned intelligent systems. The hybrid genetic algorithm-pattern search tool, embodied in the structure of committee machine, assigns a weight factor to each individual intelligent system, indicating its involvement in overall prediction of DSI parameters. This methodology was implemented in Asmari formation, which is the major carbonate reservoir rock of Iranian oil field. A group of 1,640 data points was used to construct the intelligent model, and a group of 800 data points was employed to assess the reliability of the proposed model. The results showed that the committee machine with intelligent systems performed more effectively compared with individual intelligent systems performing alone.     

Assessment of the climate change impacts on flood frequency (case study: Bazoft Basin,Iran)

The present study attempts to investigate potential impacts of climate change on floods frequency in Bazoft Basin which is located in central part of Iran. A combination of four general circulation models is used through a weighting approach to assess uncertainty in the climate projections. LARS-WG model is applied to downscale large scale atmospheric data to local stations. The resulting data is in turn used as input of the hydrological model Water and Energy Transfer between Soil, plants and atmosphere (WetSpa) to simulate runoff for present (1971–2000), near future (2020–2049) and far future (2071–2100) conditions. Results demonstrate good performance of both WetSpa and LARS-WG models. In addition in this paper instantaneous peak flow (IPF) is estimated using some empirical equations including Fuller, Sangal and Fill–Steiner methods. Comparison of estimated and observed IPF shows that Fill–Steiner is better than other methods. Then different probability distribution functions are fit to IPF series. Results of flood frequency analysis indicate that Pearson III is the best distribution fitted to IPF data. It is also indicated that flood magnitude will decrease in future for all return periods.